The nature of people’s online activities has gone through dramatic changes in past decades, yet browser interfaces have stayed largely the same since tabs were introduced nearly 20 years ago. The divide between browser interfaces that only provide affordances for managing individual tabs and users who manage their attention at the task level can lead to an “tab overload” problem. We explored a task-centric tab manager called which enables users to save their open tabs to manage them with task structures and affordances that better reflect their mental models. To lower the cost of importing, Tabs.do uses a neural network in the browser to make suggestions for grouping users’ open tabs by tasks.

BEST PAPER NOMINATION

Tabs have become integral to Web browsing yet have changed little since their introduction nearly 20 years ago. In contrast, the internet has gone through dramatic changes and increasingly used to support complex sensemaking tasks. This paper investigates how tabs today are overloaded with a diverse set of functionalities and issues users face when managing them. We uncovered competing pressures pushing for keeping tabs open (ranging from interaction to emotional costs) versus pushing for closing them (such as limited attention and resources). We further developed rich design implications for future browser interfaces.

Consumers can choose from many different products and base their decisions on the tens of thousands of online evidence about each of their options. However, to synthesize this information into confident decisions can incur high interaction and cognitive costs. Online information is scattered across different sources, and evidence such as reviews can be subjective and conflicting, requiring users to interpret them under their personal context. We introduce Mesh, which scaffolds users in iteratively building up a better understanding of both their choices by evaluating evidence gathered across sources. Lab and field deployment studies found that Mesh significantly reduces the costs of gathering and evaluating evidence and scaffolds decision-making through personalized criteria enabling users to gain deeper insights from data to make confident purchase decisions.

Whether figuring out where to eat in an unfamiliar city or deciding which apartment to live in, reviews and forum posts are often a significant factor in online decision making. However, making sense of these rich repositories of diverse opinions can be prohibitively effortful, searchers need to sift through a large number of reviews to characterize each item based on aspects that they care about. We introduce a novel system, SearchLens, where searchers build up a collection of composable and reusable “Lenses” that reflect their different latent interests. Also, the Lenses allowed the system to generate personalized interfaces with visual explanations that promote transparency and enable in-depth exploration.

Complex searches can be overwhelming, leading to lots of opened tabs. This tab overload can make conducting searches on mobile devices especially difficult where screen real-estate is limited, and progress can often be interrupted. Rather than using tabs to manage information, we introduce browsing through scaffolding. Search result lists serve as mutable workspaces where progress can be suspended and resumed. BentoBrowser is available for download from the iPhone AppStore.

Highlighting can be mentally taxing for learners who are often unsure about how much information they needed to include. We introduce the idea of intentionally uncertain input in the context of highlighting on mobile devices. We present a system that uses force touch and fuzzy bounding boxes to support saving information while users are uncertain about where to highlight.